Overview

Dataset statistics

Number of variables74
Number of observations1877
Missing cells22
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory592.1 B

Variable types

Numeric12
Categorical61
Text1

Alerts

bluetooth is highly imbalanced (72.5%)Imbalance
cd is highly imbalanced (86.6%)Imbalance
dvd is highly imbalanced (73.6%)Imbalance
hdmi is highly imbalanced (91.1%)Imbalance
radio is highly imbalanced (89.5%)Imbalance
ac is highly imbalanced (88.8%)Imbalance
alarm is highly imbalanced (99.3%)Imbalance
keyless_entry is highly imbalanced (99.3%)Imbalance
camera is highly imbalanced (98.3%)Imbalance
power_mirrors is highly imbalanced (97.8%)Imbalance
steering_wheel_controls is highly imbalanced (94.0%)Imbalance
front_cupholders is highly imbalanced (86.3%)Imbalance
rain_sensor is highly imbalanced (99.3%)Imbalance
panoramic_roof is highly imbalanced (77.0%)Imbalance
fuel_type is highly imbalanced (59.7%)Imbalance
engine_type is highly imbalanced (84.8%)Imbalance
extras is highly imbalanced (98.8%)Imbalance
driver_seat_adjustment is highly imbalanced (98.8%)Imbalance
rearview_mirror_camera is highly imbalanced (92.6%)Imbalance
wheel_material is highly imbalanced (69.3%)Imbalance
rear_window is highly imbalanced (90.8%)Imbalance
lane_departure_warning is highly imbalanced (55.6%)Imbalance
isofix_anchor is highly imbalanced (53.2%)Imbalance
brake_assist is highly imbalanced (98.3%)Imbalance
front_airbags is highly imbalanced (57.8%)Imbalance
side_airbags is highly imbalanced (62.1%)Imbalance
hill_descent_control is highly imbalanced (94.8%)Imbalance
cruise_control is highly imbalanced (98.8%)Imbalance
disc_brakes is highly imbalanced (97.8%)Imbalance
automatic_collision_braking is highly imbalanced (51.2%)Imbalance
abs_brakes is highly imbalanced (83.7%)Imbalance
disc_brakes.1 is highly imbalanced (99.3%)Imbalance
stop_go_function is highly imbalanced (83.7%)Imbalance
displacement has 22 (1.2%) missing valuesMissing
gears has 270 (14.4%) zerosZeros
max_speed has 1030 (54.9%) zerosZeros
wheels has 496 (26.4%) zerosZeros

Reproduction

Analysis started2024-06-20 01:31:03.581012
Analysis finished2024-06-20 01:32:00.618465
Duration57.04 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

fuel_consumption_km_l
Real number (ℝ)

Distinct24
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.058604
Minimum0
Maximum31
Zeros8
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:32:00.770915image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q110
median13
Q315
95-th percentile18
Maximum31
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.9756424
Coefficient of variation (CV)0.3044462
Kurtosis3.6450534
Mean13.058604
Median Absolute Deviation (MAD)2
Skewness0.90777849
Sum24511
Variance15.805732
MonotonicityNot monotonic
2024-06-20T01:32:01.264655image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
9 321
17.1%
15 260
13.9%
14 234
12.5%
13 190
10.1%
12 143
7.6%
17 135
7.2%
11 112
 
6.0%
16 105
 
5.6%
18 81
 
4.3%
8 67
 
3.6%
Other values (14) 229
12.2%
ValueCountFrequency (%)
0 8
 
0.4%
5 8
 
0.4%
6 25
 
1.3%
7 38
 
2.0%
8 67
 
3.6%
9 321
17.1%
10 67
 
3.6%
11 112
 
6.0%
12 143
7.6%
13 190
10.1%
ValueCountFrequency (%)
31 16
 
0.9%
30 2
 
0.1%
28 8
 
0.4%
27 3
 
0.2%
26 2
 
0.1%
23 1
 
0.1%
21 2
 
0.1%
20 24
 
1.3%
19 25
 
1.3%
18 81
4.3%

traccion
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
4x2
1641 
4x4
236 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5631
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4x2
2nd row4x4
3rd row4x4
4th row4x2
5th row4x4

Common Values

ValueCountFrequency (%)
4x2 1641
87.4%
4x4 236
 
12.6%

Length

2024-06-20T01:32:01.758290image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:02.196679image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
4x2 1641
87.4%
4x4 236
 
12.6%

Most occurring characters

ValueCountFrequency (%)
4 2113
37.5%
x 1877
33.3%
2 1641
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 2113
37.5%
x 1877
33.3%
2 1641
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 2113
37.5%
x 1877
33.3%
2 1641
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 2113
37.5%
x 1877
33.3%
2 1641
29.1%

transmision
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Automático
1478 
Manual
399 

Length

Max length10
Median length10
Mean length9.149707
Min length6

Characters and Unicode

Total characters17174
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutomático
2nd rowAutomático
3rd rowAutomático
4th rowAutomático
5th rowAutomático

Common Values

ValueCountFrequency (%)
Automático 1478
78.7%
Manual 399
 
21.3%

Length

2024-06-20T01:32:02.584229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:02.979314image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
automático 1478
78.7%
manual 399
 
21.3%

Most occurring characters

ValueCountFrequency (%)
t 2956
17.2%
o 2956
17.2%
u 1877
10.9%
A 1478
8.6%
m 1478
8.6%
á 1478
8.6%
i 1478
8.6%
c 1478
8.6%
a 798
 
4.6%
M 399
 
2.3%
Other values (2) 798
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2956
17.2%
o 2956
17.2%
u 1877
10.9%
A 1478
8.6%
m 1478
8.6%
á 1478
8.6%
i 1478
8.6%
c 1478
8.6%
a 798
 
4.6%
M 399
 
2.3%
Other values (2) 798
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2956
17.2%
o 2956
17.2%
u 1877
10.9%
A 1478
8.6%
m 1478
8.6%
á 1478
8.6%
i 1478
8.6%
c 1478
8.6%
a 798
 
4.6%
M 399
 
2.3%
Other values (2) 798
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2956
17.2%
o 2956
17.2%
u 1877
10.9%
A 1478
8.6%
m 1478
8.6%
á 1478
8.6%
i 1478
8.6%
c 1478
8.6%
a 798
 
4.6%
M 399
 
2.3%
Other values (2) 798
 
4.6%

aux
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1390 
No
487 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowNo
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1390
74.1%
No 487
 
25.9%

Length

2024-06-20T01:32:03.285942image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:03.697774image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1390
74.1%
no 487
 
25.9%

Most occurring characters

ValueCountFrequency (%)
S 1390
37.0%
í 1390
37.0%
N 487
 
13.0%
o 487
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1390
37.0%
í 1390
37.0%
N 487
 
13.0%
o 487
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1390
37.0%
í 1390
37.0%
N 487
 
13.0%
o 487
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1390
37.0%
í 1390
37.0%
N 487
 
13.0%
o 487
 
13.0%

android_auto
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1401 
Sí
476 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSí
5th rowNo

Common Values

ValueCountFrequency (%)
No 1401
74.6%
Sí 476
 
25.4%

Length

2024-06-20T01:32:04.168525image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:04.660891image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1401
74.6%
sí 476
 
25.4%

Most occurring characters

ValueCountFrequency (%)
N 1401
37.3%
o 1401
37.3%
S 476
 
12.7%
í 476
 
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1401
37.3%
o 1401
37.3%
S 476
 
12.7%
í 476
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1401
37.3%
o 1401
37.3%
S 476
 
12.7%
í 476
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1401
37.3%
o 1401
37.3%
S 476
 
12.7%
í 476
 
12.7%

apple_carplay
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1398 
Sí
479 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSí
5th rowNo

Common Values

ValueCountFrequency (%)
No 1398
74.5%
Sí 479
 
25.5%

Length

2024-06-20T01:32:04.966563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:05.284229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1398
74.5%
sí 479
 
25.5%

Most occurring characters

ValueCountFrequency (%)
N 1398
37.2%
o 1398
37.2%
S 479
 
12.8%
í 479
 
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1398
37.2%
o 1398
37.2%
S 479
 
12.8%
í 479
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1398
37.2%
o 1398
37.2%
S 479
 
12.8%
í 479
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1398
37.2%
o 1398
37.2%
S 479
 
12.8%
í 479
 
12.8%

bluetooth
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1788 
Sí
 
89

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowNo

Common Values

ValueCountFrequency (%)
No 1788
95.3%
Sí 89
 
4.7%

Length

2024-06-20T01:32:05.584929image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:05.995006image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1788
95.3%
sí 89
 
4.7%

Most occurring characters

ValueCountFrequency (%)
N 1788
47.6%
o 1788
47.6%
S 89
 
2.4%
í 89
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1788
47.6%
o 1788
47.6%
S 89
 
2.4%
í 89
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1788
47.6%
o 1788
47.6%
S 89
 
2.4%
í 89
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1788
47.6%
o 1788
47.6%
S 89
 
2.4%
í 89
 
2.4%

cd
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1842 
Sí
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSí
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1842
98.1%
Sí 35
 
1.9%

Length

2024-06-20T01:32:06.463008image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:06.897216image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1842
98.1%
sí 35
 
1.9%

Most occurring characters

ValueCountFrequency (%)
N 1842
49.1%
o 1842
49.1%
S 35
 
0.9%
í 35
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1842
49.1%
o 1842
49.1%
S 35
 
0.9%
í 35
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1842
49.1%
o 1842
49.1%
S 35
 
0.9%
í 35
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1842
49.1%
o 1842
49.1%
S 35
 
0.9%
í 35
 
0.9%

dvd
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1793 
Sí
 
84

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1793
95.5%
Sí 84
 
4.5%

Length

2024-06-20T01:32:07.293376image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:07.796476image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1793
95.5%
sí 84
 
4.5%

Most occurring characters

ValueCountFrequency (%)
N 1793
47.8%
o 1793
47.8%
S 84
 
2.2%
í 84
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1793
47.8%
o 1793
47.8%
S 84
 
2.2%
í 84
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1793
47.8%
o 1793
47.8%
S 84
 
2.2%
í 84
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1793
47.8%
o 1793
47.8%
S 84
 
2.2%
í 84
 
2.2%

hdmi
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1856 
Sí
 
21

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1856
98.9%
Sí 21
 
1.1%

Length

2024-06-20T01:32:08.164670image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:08.590186image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1856
98.9%
sí 21
 
1.1%

Most occurring characters

ValueCountFrequency (%)
N 1856
49.4%
o 1856
49.4%
S 21
 
0.6%
í 21
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1856
49.4%
o 1856
49.4%
S 21
 
0.6%
í 21
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1856
49.4%
o 1856
49.4%
S 21
 
0.6%
í 21
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1856
49.4%
o 1856
49.4%
S 21
 
0.6%
í 21
 
0.6%

touch_screen
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
961 
No
916 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowNo
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 961
51.2%
No 916
48.8%

Length

2024-06-20T01:32:08.893857image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:09.281286image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 961
51.2%
no 916
48.8%

Most occurring characters

ValueCountFrequency (%)
S 961
25.6%
í 961
25.6%
N 916
24.4%
o 916
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 961
25.6%
í 961
25.6%
N 916
24.4%
o 916
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 961
25.6%
í 961
25.6%
N 916
24.4%
o 916
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 961
25.6%
í 961
25.6%
N 916
24.4%
o 916
24.4%

radio
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1851 
No
 
26

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1851
98.6%
No 26
 
1.4%

Length

2024-06-20T01:32:09.657747image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:10.002281image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1851
98.6%
no 26
 
1.4%

Most occurring characters

ValueCountFrequency (%)
S 1851
49.3%
í 1851
49.3%
N 26
 
0.7%
o 26
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1851
49.3%
í 1851
49.3%
N 26
 
0.7%
o 26
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1851
49.3%
í 1851
49.3%
N 26
 
0.7%
o 26
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1851
49.3%
í 1851
49.3%
N 26
 
0.7%
o 26
 
0.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1245 
Sí
632 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowNo
5th rowSí

Common Values

ValueCountFrequency (%)
No 1245
66.3%
Sí 632
33.7%

Length

2024-06-20T01:32:10.362123image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:10.765578image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1245
66.3%
sí 632
33.7%

Most occurring characters

ValueCountFrequency (%)
N 1245
33.2%
o 1245
33.2%
S 632
16.8%
í 632
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1245
33.2%
o 1245
33.2%
S 632
16.8%
í 632
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1245
33.2%
o 1245
33.2%
S 632
16.8%
í 632
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1245
33.2%
o 1245
33.2%
S 632
16.8%
í 632
16.8%

sd_card
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1449 
Sí
428 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowNo
4th rowSí
5th rowNo

Common Values

ValueCountFrequency (%)
No 1449
77.2%
Sí 428
 
22.8%

Length

2024-06-20T01:32:11.178468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:11.591413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1449
77.2%
sí 428
 
22.8%

Most occurring characters

ValueCountFrequency (%)
N 1449
38.6%
o 1449
38.6%
S 428
 
11.4%
í 428
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1449
38.6%
o 1449
38.6%
S 428
 
11.4%
í 428
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1449
38.6%
o 1449
38.6%
S 428
 
11.4%
í 428
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1449
38.6%
o 1449
38.6%
S 428
 
11.4%
í 428
 
11.4%

ac
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1849 
No
 
28

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1849
98.5%
No 28
 
1.5%

Length

2024-06-20T01:32:11.957554image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:12.358003image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1849
98.5%
no 28
 
1.5%

Most occurring characters

ValueCountFrequency (%)
S 1849
49.3%
í 1849
49.3%
N 28
 
0.7%
o 28
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1849
49.3%
í 1849
49.3%
N 28
 
0.7%
o 28
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1849
49.3%
í 1849
49.3%
N 28
 
0.7%
o 28
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1849
49.3%
í 1849
49.3%
N 28
 
0.7%
o 28
 
0.7%

alarm
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1876 
Sí
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1876
99.9%
Sí 1
 
0.1%

Length

2024-06-20T01:32:12.682293image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:13.173047image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1876
99.9%
sí 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

keyless_entry
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1876 
Sí
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1876
99.9%
Sí 1
 
0.1%

Length

2024-06-20T01:32:13.570195image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:13.883758image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1876
99.9%
sí 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

push_start
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1049 
No
828 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1049
55.9%
No 828
44.1%

Length

2024-06-20T01:32:14.558320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:14.983137image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1049
55.9%
no 828
44.1%

Most occurring characters

ValueCountFrequency (%)
S 1049
27.9%
í 1049
27.9%
N 828
22.1%
o 828
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1049
27.9%
í 1049
27.9%
N 828
22.1%
o 828
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1049
27.9%
í 1049
27.9%
N 828
22.1%
o 828
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1049
27.9%
í 1049
27.9%
N 828
22.1%
o 828
22.1%

camera
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1874 
Sí
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1874
99.8%
Sí 3
 
0.2%

Length

2024-06-20T01:32:15.468220image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:15.895842image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1874
99.8%
sí 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 1874
49.9%
o 1874
49.9%
S 3
 
0.1%
í 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1874
49.9%
o 1874
49.9%
S 3
 
0.1%
í 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1874
49.9%
o 1874
49.9%
S 3
 
0.1%
í 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1874
49.9%
o 1874
49.9%
S 3
 
0.1%
í 3
 
0.1%

power_mirrors
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1873 
Sí
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1873
99.8%
Sí 4
 
0.2%

Length

2024-06-20T01:32:16.363296image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:16.772434image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1873
99.8%
sí 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 1873
49.9%
o 1873
49.9%
S 4
 
0.1%
í 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1873
49.9%
o 1873
49.9%
S 4
 
0.1%
í 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1873
49.9%
o 1873
49.9%
S 4
 
0.1%
í 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1873
49.9%
o 1873
49.9%
S 4
 
0.1%
í 4
 
0.1%

steering_wheel_controls
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1864 
Sí
 
13

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1864
99.3%
Sí 13
 
0.7%

Length

2024-06-20T01:32:17.084674image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:17.563418image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1864
99.3%
sí 13
 
0.7%

Most occurring characters

ValueCountFrequency (%)
N 1864
49.7%
o 1864
49.7%
S 13
 
0.3%
í 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1864
49.7%
o 1864
49.7%
S 13
 
0.3%
í 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1864
49.7%
o 1864
49.7%
S 13
 
0.3%
í 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1864
49.7%
o 1864
49.7%
S 13
 
0.3%
í 13
 
0.3%

front_cupholders
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1841 
No
 
36

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1841
98.1%
No 36
 
1.9%

Length

2024-06-20T01:32:17.968529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:18.380674image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1841
98.1%
no 36
 
1.9%

Most occurring characters

ValueCountFrequency (%)
S 1841
49.0%
í 1841
49.0%
N 36
 
1.0%
o 36
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1841
49.0%
í 1841
49.0%
N 36
 
1.0%
o 36
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1841
49.0%
í 1841
49.0%
N 36
 
1.0%
o 36
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1841
49.0%
í 1841
49.0%
N 36
 
1.0%
o 36
 
1.0%

rear_cupholders
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1337 
No
540 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1337
71.2%
No 540
28.8%

Length

2024-06-20T01:32:18.683622image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:19.080383image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1337
71.2%
no 540
28.8%

Most occurring characters

ValueCountFrequency (%)
S 1337
35.6%
í 1337
35.6%
N 540
14.4%
o 540
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1337
35.6%
í 1337
35.6%
N 540
14.4%
o 540
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1337
35.6%
í 1337
35.6%
N 540
14.4%
o 540
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1337
35.6%
í 1337
35.6%
N 540
14.4%
o 540
14.4%

sunroof
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1097 
Sí
780 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowNo
5th rowSí

Common Values

ValueCountFrequency (%)
No 1097
58.4%
Sí 780
41.6%

Length

2024-06-20T01:32:19.386310image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:19.783819image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1097
58.4%
sí 780
41.6%

Most occurring characters

ValueCountFrequency (%)
N 1097
29.2%
o 1097
29.2%
S 780
20.8%
í 780
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1097
29.2%
o 1097
29.2%
S 780
20.8%
í 780
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1097
29.2%
o 1097
29.2%
S 780
20.8%
í 780
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1097
29.2%
o 1097
29.2%
S 780
20.8%
í 780
20.8%

front_sensor
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1489 
Sí
388 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
No 1489
79.3%
Sí 388
 
20.7%

Length

2024-06-20T01:32:20.184603image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:20.670460image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1489
79.3%
sí 388
 
20.7%

Most occurring characters

ValueCountFrequency (%)
N 1489
39.7%
o 1489
39.7%
S 388
 
10.3%
í 388
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1489
39.7%
o 1489
39.7%
S 388
 
10.3%
í 388
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1489
39.7%
o 1489
39.7%
S 388
 
10.3%
í 388
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1489
39.7%
o 1489
39.7%
S 388
 
10.3%
í 388
 
10.3%

rear_sensor
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1184 
No
693 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1184
63.1%
No 693
36.9%

Length

2024-06-20T01:32:20.993272image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:21.478144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1184
63.1%
no 693
36.9%

Most occurring characters

ValueCountFrequency (%)
S 1184
31.5%
í 1184
31.5%
N 693
18.5%
o 693
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1184
31.5%
í 1184
31.5%
N 693
18.5%
o 693
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1184
31.5%
í 1184
31.5%
N 693
18.5%
o 693
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1184
31.5%
í 1184
31.5%
N 693
18.5%
o 693
18.5%

rain_sensor
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1876 
Sí
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1876
99.9%
Sí 1
 
0.1%

Length

2024-06-20T01:32:21.871719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:22.304955image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1876
99.9%
sí 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

panoramic_roof
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1807 
Sí
 
70

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1807
96.3%
Sí 70
 
3.7%

Length

2024-06-20T01:32:22.722104image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:23.193471image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1807
96.3%
sí 70
 
3.7%

Most occurring characters

ValueCountFrequency (%)
N 1807
48.1%
o 1807
48.1%
S 70
 
1.9%
í 70
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1807
48.1%
o 1807
48.1%
S 70
 
1.9%
í 70
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1807
48.1%
o 1807
48.1%
S 70
 
1.9%
í 70
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1807
48.1%
o 1807
48.1%
S 70
 
1.9%
í 70
 
1.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1497 
No
380 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1497
79.8%
No 380
 
20.2%

Length

2024-06-20T01:32:23.578332image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:24.007664image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1497
79.8%
no 380
 
20.2%

Most occurring characters

ValueCountFrequency (%)
S 1497
39.9%
í 1497
39.9%
N 380
 
10.1%
o 380
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1497
39.9%
í 1497
39.9%
N 380
 
10.1%
o 380
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1497
39.9%
í 1497
39.9%
N 380
 
10.1%
o 380
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1497
39.9%
í 1497
39.9%
N 380
 
10.1%
o 380
 
10.1%

horsepower
Real number (ℝ)

Distinct196
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.08737
Minimum66
Maximum557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:32:24.460014image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile89
Q1120
median154
Q3201
95-th percentile310
Maximum557
Range491
Interquartile range (IQR)81

Descriptive statistics

Standard deviation74.49669
Coefficient of variation (CV)0.42548294
Kurtosis1.8081341
Mean175.08737
Median Absolute Deviation (MAD)36
Skewness1.2906753
Sum328639
Variance5549.7568
MonotonicityNot monotonic
2024-06-20T01:32:24.972204image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121 59
 
3.1%
150 54
 
2.9%
181 54
 
2.9%
140 52
 
2.8%
118 51
 
2.7%
110 49
 
2.6%
106 44
 
2.3%
188 42
 
2.2%
290 40
 
2.1%
148 39
 
2.1%
Other values (186) 1393
74.2%
ValueCountFrequency (%)
66 5
 
0.3%
69 8
0.4%
71 1
 
0.1%
76 17
0.9%
81 18
1.0%
82 11
0.6%
83 4
 
0.2%
84 4
 
0.2%
85 10
0.5%
86 3
 
0.2%
ValueCountFrequency (%)
557 1
 
0.1%
547 1
 
0.1%
520 1
 
0.1%
510 1
 
0.1%
460 4
 
0.2%
450 1
 
0.1%
449 2
 
0.1%
444 1
 
0.1%
429 1
 
0.1%
420 10
0.5%

fuel_type
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Gasolina
1385 
estándar
425 
Híbrido
 
32
Diesel
 
16
NoInfo
 
16

Length

Max length9
Median length8
Mean length7.9504529
Min length6

Characters and Unicode

Total characters14923
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowestándar
2nd rowestándar
3rd rowestándar
4th rowestándar
5th rowGasolina

Common Values

ValueCountFrequency (%)
Gasolina 1385
73.8%
estándar 425
 
22.6%
Híbrido 32
 
1.7%
Diesel 16
 
0.9%
NoInfo 16
 
0.9%
Eléctrico 3
 
0.2%

Length

2024-06-20T01:32:25.470121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:25.981611image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
gasolina 1385
73.8%
estándar 425
 
22.6%
híbrido 32
 
1.7%
diesel 16
 
0.9%
noinfo 16
 
0.9%
eléctrico 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 3195
21.4%
s 1826
12.2%
n 1826
12.2%
o 1452
9.7%
i 1436
9.6%
l 1404
9.4%
G 1385
9.3%
r 460
 
3.1%
d 457
 
3.1%
e 457
 
3.1%
Other values (12) 1025
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3195
21.4%
s 1826
12.2%
n 1826
12.2%
o 1452
9.7%
i 1436
9.6%
l 1404
9.4%
G 1385
9.3%
r 460
 
3.1%
d 457
 
3.1%
e 457
 
3.1%
Other values (12) 1025
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3195
21.4%
s 1826
12.2%
n 1826
12.2%
o 1452
9.7%
i 1436
9.6%
l 1404
9.4%
G 1385
9.3%
r 460
 
3.1%
d 457
 
3.1%
e 457
 
3.1%
Other values (12) 1025
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3195
21.4%
s 1826
12.2%
n 1826
12.2%
o 1452
9.7%
i 1436
9.6%
l 1404
9.4%
G 1385
9.3%
r 460
 
3.1%
d 457
 
3.1%
e 457
 
3.1%
Other values (12) 1025
 
6.9%

displacement
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)1.7%
Missing22
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2.0921294
Minimum0
Maximum6.2
Zeros6
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:32:26.470727image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q11.6
median2
Q32.4
95-th percentile3.6
Maximum6.2
Range6.2
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.85798649
Coefficient of variation (CV)0.41010202
Kurtosis4.2891834
Mean2.0921294
Median Absolute Deviation (MAD)0.4
Skewness1.7944356
Sum3880.9
Variance0.73614082
MonotonicityNot monotonic
2024-06-20T01:32:26.923064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 401
21.4%
1.6 346
18.4%
1.4 165
8.8%
1.5 152
 
8.1%
2.5 151
 
8.0%
1.8 118
 
6.3%
2.4 90
 
4.8%
3.5 84
 
4.5%
1.2 83
 
4.4%
3.6 77
 
4.1%
Other values (21) 188
10.0%
ValueCountFrequency (%)
0 6
 
0.3%
0.6 1
 
0.1%
1 30
 
1.6%
1.2 83
 
4.4%
1.3 10
 
0.5%
1.4 165
8.8%
1.5 152
 
8.1%
1.6 346
18.4%
1.8 118
 
6.3%
2 401
21.4%
ValueCountFrequency (%)
6.2 8
0.4%
5.7 10
0.5%
5.6 1
 
0.1%
5.5 2
 
0.1%
5.3 5
 
0.3%
5 10
0.5%
4.8 6
 
0.3%
4.4 2
 
0.1%
4 4
 
0.2%
3.7 19
1.0%

gears
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2871604
Minimum0
Maximum10
Zeros270
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:32:27.357100image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4427584
Coefficient of variation (CV)0.46201708
Kurtosis0.64392746
Mean5.2871604
Median Absolute Deviation (MAD)1
Skewness-1.1551278
Sum9924
Variance5.9670685
MonotonicityNot monotonic
2024-06-20T01:32:27.770594image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6 766
40.8%
5 315
16.8%
0 270
 
14.4%
7 204
 
10.9%
8 173
 
9.2%
4 70
 
3.7%
9 57
 
3.0%
10 14
 
0.7%
1 8
 
0.4%
ValueCountFrequency (%)
0 270
 
14.4%
1 8
 
0.4%
4 70
 
3.7%
5 315
16.8%
6 766
40.8%
7 204
 
10.9%
8 173
 
9.2%
9 57
 
3.0%
10 14
 
0.7%
ValueCountFrequency (%)
10 14
 
0.7%
9 57
 
3.0%
8 173
 
9.2%
7 204
 
10.9%
6 766
40.8%
5 315
16.8%
4 70
 
3.7%
1 8
 
0.4%
0 270
 
14.4%

start_stop
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1336 
Sí
541 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
No 1336
71.2%
Sí 541
28.8%

Length

2024-06-20T01:32:28.221183image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:28.677924image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1336
71.2%
sí 541
28.8%

Most occurring characters

ValueCountFrequency (%)
N 1336
35.6%
o 1336
35.6%
S 541
14.4%
í 541
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1336
35.6%
o 1336
35.6%
S 541
14.4%
í 541
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1336
35.6%
o 1336
35.6%
S 541
14.4%
í 541
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1336
35.6%
o 1336
35.6%
S 541
14.4%
í 541
14.4%

engine_type
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Combustión
1777 
Eléctrico
 
41
NoInfo
 
41
Híbrido
 
8
Mild Hybrid
 
8

Length

Max length18
Median length10
Mean length9.8907832
Min length6

Characters and Unicode

Total characters18565
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCombustión
2nd rowCombustión
3rd rowCombustión
4th rowCombustión
5th rowCombustión

Common Values

ValueCountFrequency (%)
Combustión 1777
94.7%
Eléctrico 41
 
2.2%
NoInfo 41
 
2.2%
Híbrido 8
 
0.4%
Mild Hybrid 8
 
0.4%
Motor Supercargado 2
 
0.1%

Length

2024-06-20T01:32:29.176864image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:29.697157image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
combustión 1777
94.2%
eléctrico 41
 
2.2%
noinfo 41
 
2.2%
híbrido 8
 
0.4%
mild 8
 
0.4%
hybrid 8
 
0.4%
motor 2
 
0.1%
supercargado 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 1914
10.3%
i 1842
9.9%
t 1820
9.8%
n 1818
9.8%
b 1793
9.7%
u 1779
9.6%
C 1777
9.6%
m 1777
9.6%
s 1777
9.6%
ó 1777
9.6%
Other values (19) 491
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1914
10.3%
i 1842
9.9%
t 1820
9.8%
n 1818
9.8%
b 1793
9.7%
u 1779
9.6%
C 1777
9.6%
m 1777
9.6%
s 1777
9.6%
ó 1777
9.6%
Other values (19) 491
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1914
10.3%
i 1842
9.9%
t 1820
9.8%
n 1818
9.8%
b 1793
9.7%
u 1779
9.6%
C 1777
9.6%
m 1777
9.6%
s 1777
9.6%
ó 1777
9.6%
Other values (19) 491
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1914
10.3%
i 1842
9.9%
t 1820
9.8%
n 1818
9.8%
b 1793
9.7%
u 1779
9.6%
C 1777
9.6%
m 1777
9.6%
s 1777
9.6%
ó 1777
9.6%
Other values (19) 491
 
2.6%

torque
Real number (ℝ)

Distinct172
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.30954
Minimum67
Maximum561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:32:30.325256image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile89
Q1114
median170
Q3234
95-th percentile310
Maximum561
Range494
Interquartile range (IQR)120

Descriptive statistics

Standard deviation75.809713
Coefficient of variation (CV)0.42278684
Kurtosis2.0334028
Mean179.30954
Median Absolute Deviation (MAD)56
Skewness1.2288577
Sum336564
Variance5747.1126
MonotonicityNot monotonic
2024-06-20T01:32:30.763609image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 147
 
7.8%
114 66
 
3.5%
148 60
 
3.2%
105 58
 
3.1%
111 55
 
2.9%
258 50
 
2.7%
162 49
 
2.6%
177 47
 
2.5%
260 43
 
2.3%
113 38
 
2.0%
Other values (162) 1264
67.3%
ValueCountFrequency (%)
67 3
 
0.2%
68 8
0.4%
69 5
 
0.3%
74 17
0.9%
80 18
1.0%
83 11
0.6%
84 5
 
0.3%
86 4
 
0.2%
88 13
0.7%
89 18
1.0%
ValueCountFrequency (%)
561 1
 
0.1%
516 4
0.2%
502 1
 
0.1%
479 2
0.1%
470 1
 
0.1%
461 1
 
0.1%
460 3
0.2%
450 4
0.2%
443 1
 
0.1%
428 1
 
0.1%

turbo
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1248 
Sí
629 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
No 1248
66.5%
Sí 629
33.5%

Length

2024-06-20T01:32:31.161466image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:31.574606image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1248
66.5%
sí 629
33.5%

Most occurring characters

ValueCountFrequency (%)
N 1248
33.2%
o 1248
33.2%
S 629
16.8%
í 629
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1248
33.2%
o 1248
33.2%
S 629
16.8%
í 629
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1248
33.2%
o 1248
33.2%
S 629
16.8%
í 629
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1248
33.2%
o 1248
33.2%
S 629
16.8%
í 629
16.8%

max_speed
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.404901
Minimum0
Maximum278
Zeros1030
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:32:31.960192image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3190
95-th percentile237
Maximum278
Range278
Interquartile range (IQR)190

Descriptive statistics

Standard deviation101.36602
Coefficient of variation (CV)1.1212447
Kurtosis-1.7852878
Mean90.404901
Median Absolute Deviation (MAD)0
Skewness0.29419883
Sum169690
Variance10275.069
MonotonicityNot monotonic
2024-06-20T01:32:32.409882image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1030
54.9%
180 92
 
4.9%
250 45
 
2.4%
190 41
 
2.2%
210 38
 
2.0%
164 35
 
1.9%
175 33
 
1.8%
235 28
 
1.5%
195 28
 
1.5%
224 25
 
1.3%
Other values (73) 482
25.7%
ValueCountFrequency (%)
0 1030
54.9%
140 4
 
0.2%
145 2
 
0.1%
150 4
 
0.2%
151 2
 
0.1%
152 4
 
0.2%
154 4
 
0.2%
160 7
 
0.4%
161 3
 
0.2%
162 3
 
0.2%
ValueCountFrequency (%)
278 1
 
0.1%
270 1
 
0.1%
259 2
 
0.1%
254 2
 
0.1%
250 45
2.4%
249 1
 
0.1%
248 2
 
0.1%
247 7
 
0.4%
246 2
 
0.1%
244 5
 
0.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1514 
No
363 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1514
80.7%
No 363
 
19.3%

Length

2024-06-20T01:32:32.891286image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:33.360904image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1514
80.7%
no 363
 
19.3%

Most occurring characters

ValueCountFrequency (%)
S 1514
40.3%
í 1514
40.3%
N 363
 
9.7%
o 363
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1514
40.3%
í 1514
40.3%
N 363
 
9.7%
o 363
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1514
40.3%
í 1514
40.3%
N 363
 
9.7%
o 363
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1514
40.3%
í 1514
40.3%
N 363
 
9.7%
o 363
 
9.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1540 
No
337 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1540
82.0%
No 337
 
18.0%

Length

2024-06-20T01:32:33.774734image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:34.266451image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1540
82.0%
no 337
 
18.0%

Most occurring characters

ValueCountFrequency (%)
S 1540
41.0%
í 1540
41.0%
N 337
 
9.0%
o 337
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1540
41.0%
í 1540
41.0%
N 337
 
9.0%
o 337
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1540
41.0%
í 1540
41.0%
N 337
 
9.0%
o 337
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1540
41.0%
í 1540
41.0%
N 337
 
9.0%
o 337
 
9.0%

extras
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1875 
Sí
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1875
99.9%
Sí 2
 
0.1%

Length

2024-06-20T01:32:34.676572image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:35.165162image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1875
99.9%
sí 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

driver_seat_adjustment
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1875 
Sí
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1875
99.9%
Sí 2
 
0.1%

Length

2024-06-20T01:32:35.577281image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:36.081432image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1875
99.9%
sí 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

rearview_mirror_camera
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1860 
Sí
 
17

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1860
99.1%
Sí 17
 
0.9%

Length

2024-06-20T01:32:36.558336image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:37.072017image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1860
99.1%
sí 17
 
0.9%

Most occurring characters

ValueCountFrequency (%)
N 1860
49.5%
o 1860
49.5%
S 17
 
0.5%
í 17
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1860
49.5%
o 1860
49.5%
S 17
 
0.5%
í 17
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1860
49.5%
o 1860
49.5%
S 17
 
0.5%
í 17
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1860
49.5%
o 1860
49.5%
S 17
 
0.5%
í 17
 
0.5%

seat_material
Categorical

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Tela
859 
Piel
732 
NoInfo
112 
Cuero Sintético
 
75
Tela/Piel
 
37
Other values (5)
 
62

Length

Max length16
Median length4
Mean length5.015983
Min length4

Characters and Unicode

Total characters9415
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowPiel
2nd rowPiel premium
3rd rowPiel
4th rowGamuza Sintética
5th rowPiel

Common Values

ValueCountFrequency (%)
Tela 859
45.8%
Piel 732
39.0%
NoInfo 112
 
6.0%
Cuero Sintético 75
 
4.0%
Tela/Piel 37
 
2.0%
Tela/Terciopelo 33
 
1.8%
Gamuza Sintética 22
 
1.2%
Piel premium 4
 
0.2%
Gamuza 2
 
0.1%
Piel perforada 1
 
0.1%

Length

2024-06-20T01:32:37.478579image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:38.005822image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
tela 859
43.4%
piel 737
37.2%
noinfo 112
 
5.7%
cuero 75
 
3.8%
sintético 75
 
3.8%
tela/piel 37
 
1.9%
tela/terciopelo 33
 
1.7%
gamuza 24
 
1.2%
sintética 22
 
1.1%
premium 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 1849
19.6%
l 1736
18.4%
i 1005
10.7%
a 1001
10.6%
T 962
10.2%
P 774
8.2%
o 441
 
4.7%
n 209
 
2.2%
t 194
 
2.1%
c 130
 
1.4%
Other values (15) 1114
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1849
19.6%
l 1736
18.4%
i 1005
10.7%
a 1001
10.6%
T 962
10.2%
P 774
8.2%
o 441
 
4.7%
n 209
 
2.2%
t 194
 
2.1%
c 130
 
1.4%
Other values (15) 1114
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1849
19.6%
l 1736
18.4%
i 1005
10.7%
a 1001
10.6%
T 962
10.2%
P 774
8.2%
o 441
 
4.7%
n 209
 
2.2%
t 194
 
2.1%
c 130
 
1.4%
Other values (15) 1114
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1849
19.6%
l 1736
18.4%
i 1005
10.7%
a 1001
10.6%
T 962
10.2%
P 774
8.2%
o 441
 
4.7%
n 209
 
2.2%
t 194
 
2.1%
c 130
 
1.4%
Other values (15) 1114
11.8%

passengers
Real number (ℝ)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0490144
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:32:38.377550image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q35
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.91931729
Coefficient of variation (CV)0.18207856
Kurtosis4.1236843
Mean5.0490144
Median Absolute Deviation (MAD)0
Skewness0.21609889
Sum9477
Variance0.84514428
MonotonicityNot monotonic
2024-06-20T01:32:38.763849image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 1471
78.4%
7 154
 
8.2%
4 139
 
7.4%
2 48
 
2.6%
8 34
 
1.8%
3 22
 
1.2%
6 9
 
0.5%
ValueCountFrequency (%)
2 48
 
2.6%
3 22
 
1.2%
4 139
 
7.4%
5 1471
78.4%
6 9
 
0.5%
7 154
 
8.2%
8 34
 
1.8%
ValueCountFrequency (%)
8 34
 
1.8%
7 154
 
8.2%
6 9
 
0.5%
5 1471
78.4%
4 139
 
7.4%
3 22
 
1.2%
2 48
 
2.6%

trunk_opening
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Remota
1225 
NoInfo
488 
Eléctrica
 
94
Mecánica
 
70

Length

Max length9
Median length6
Mean length6.2248269
Min length6

Characters and Unicode

Total characters11684
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRemota
2nd rowRemota
3rd rowRemota
4th rowRemota
5th rowRemota

Common Values

ValueCountFrequency (%)
Remota 1225
65.3%
NoInfo 488
 
26.0%
Eléctrica 94
 
5.0%
Mecánica 70
 
3.7%

Length

2024-06-20T01:32:39.196482image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:39.786541image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
remota 1225
65.3%
noinfo 488
 
26.0%
eléctrica 94
 
5.0%
mecánica 70
 
3.7%

Most occurring characters

ValueCountFrequency (%)
o 2201
18.8%
a 1389
11.9%
t 1319
11.3%
e 1295
11.1%
R 1225
10.5%
m 1225
10.5%
n 558
 
4.8%
f 488
 
4.2%
I 488
 
4.2%
N 488
 
4.2%
Other values (8) 1008
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2201
18.8%
a 1389
11.9%
t 1319
11.3%
e 1295
11.1%
R 1225
10.5%
m 1225
10.5%
n 558
 
4.8%
f 488
 
4.2%
I 488
 
4.2%
N 488
 
4.2%
Other values (8) 1008
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2201
18.8%
a 1389
11.9%
t 1319
11.3%
e 1295
11.1%
R 1225
10.5%
m 1225
10.5%
n 558
 
4.8%
f 488
 
4.2%
I 488
 
4.2%
N 488
 
4.2%
Other values (8) 1008
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2201
18.8%
a 1389
11.9%
t 1319
11.3%
e 1295
11.1%
R 1225
10.5%
m 1225
10.5%
n 558
 
4.8%
f 488
 
4.2%
I 488
 
4.2%
N 488
 
4.2%
Other values (8) 1008
8.6%

headlights
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
NoInfo
1052 
Estándar
819 
Faros de Niebla
 
5
Faros Halógenos
 
1

Length

Max length15
Median length6
Mean length6.9014385
Min length6

Characters and Unicode

Total characters12954
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEstándar
2nd rowNoInfo
3rd rowNoInfo
4th rowEstándar
5th rowEstándar

Common Values

ValueCountFrequency (%)
NoInfo 1052
56.0%
Estándar 819
43.6%
Faros de Niebla 5
 
0.3%
Faros Halógenos 1
 
0.1%

Length

2024-06-20T01:32:40.207958image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:40.772864image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
noinfo 1052
55.7%
estándar 819
43.4%
faros 6
 
0.3%
de 5
 
0.3%
niebla 5
 
0.3%
halógenos 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 2111
16.3%
n 1872
14.5%
N 1057
8.2%
I 1052
8.1%
f 1052
8.1%
a 831
 
6.4%
s 826
 
6.4%
r 825
 
6.4%
d 824
 
6.4%
t 819
 
6.3%
Other values (11) 1685
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12954
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2111
16.3%
n 1872
14.5%
N 1057
8.2%
I 1052
8.1%
f 1052
8.1%
a 831
 
6.4%
s 826
 
6.4%
r 825
 
6.4%
d 824
 
6.4%
t 819
 
6.3%
Other values (11) 1685
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12954
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2111
16.3%
n 1872
14.5%
N 1057
8.2%
I 1052
8.1%
f 1052
8.1%
a 831
 
6.4%
s 826
 
6.4%
r 825
 
6.4%
d 824
 
6.4%
t 819
 
6.3%
Other values (11) 1685
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12954
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2111
16.3%
n 1872
14.5%
N 1057
8.2%
I 1052
8.1%
f 1052
8.1%
a 831
 
6.4%
s 826
 
6.4%
r 825
 
6.4%
d 824
 
6.4%
t 819
 
6.3%
Other values (11) 1685
13.0%

wheel_material
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Aluminio
1674 
Aleación
 
134
NoInfo
 
61
Aluminio Cromado
 
8

Length

Max length16
Median length8
Mean length7.9690996
Min length6

Characters and Unicode

Total characters14958
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAluminio
2nd rowAluminio
3rd rowAluminio
4th rowAluminio
5th rowAluminio

Common Values

ValueCountFrequency (%)
Aluminio 1674
89.2%
Aleación 134
 
7.1%
NoInfo 61
 
3.2%
Aluminio Cromado 8
 
0.4%

Length

2024-06-20T01:32:41.195876image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:41.685737image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
aluminio 1682
89.2%
aleación 134
 
7.1%
noinfo 61
 
3.2%
cromado 8
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 3498
23.4%
n 1877
12.5%
o 1820
12.2%
A 1816
12.1%
l 1816
12.1%
m 1690
11.3%
u 1682
11.2%
a 142
 
0.9%
ó 134
 
0.9%
c 134
 
0.9%
Other values (8) 349
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3498
23.4%
n 1877
12.5%
o 1820
12.2%
A 1816
12.1%
l 1816
12.1%
m 1690
11.3%
u 1682
11.2%
a 142
 
0.9%
ó 134
 
0.9%
c 134
 
0.9%
Other values (8) 349
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3498
23.4%
n 1877
12.5%
o 1820
12.2%
A 1816
12.1%
l 1816
12.1%
m 1690
11.3%
u 1682
11.2%
a 142
 
0.9%
ó 134
 
0.9%
c 134
 
0.9%
Other values (8) 349
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3498
23.4%
n 1877
12.5%
o 1820
12.2%
A 1816
12.1%
l 1816
12.1%
m 1690
11.3%
u 1682
11.2%
a 142
 
0.9%
ó 134
 
0.9%
c 134
 
0.9%
Other values (8) 349
 
2.3%

doors
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
5
1160 
4
563 
3
 
88
2
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1877
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 1160
61.8%
4 563
30.0%
3 88
 
4.7%
2 66
 
3.5%

Length

2024-06-20T01:32:42.079630image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:42.499364image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
5 1160
61.8%
4 563
30.0%
3 88
 
4.7%
2 66
 
3.5%

Most occurring characters

ValueCountFrequency (%)
5 1160
61.8%
4 563
30.0%
3 88
 
4.7%
2 66
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1160
61.8%
4 563
30.0%
3 88
 
4.7%
2 66
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1160
61.8%
4 563
30.0%
3 88
 
4.7%
2 66
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1160
61.8%
4 563
30.0%
3 88
 
4.7%
2 66
 
3.5%

wheels
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.392648
Minimum0
Maximum22
Zeros496
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:32:42.961177image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16
Q317
95-th percentile19
Maximum22
Range22
Interquartile range (IQR)17

Descriptive statistics

Standard deviation7.5494679
Coefficient of variation (CV)0.60918926
Kurtosis-0.91607538
Mean12.392648
Median Absolute Deviation (MAD)2
Skewness-0.96205951
Sum23261
Variance56.994466
MonotonicityNot monotonic
2024-06-20T01:32:43.387517image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 496
26.4%
17 366
19.5%
16 285
15.2%
18 242
12.9%
15 201
10.7%
19 114
 
6.1%
14 88
 
4.7%
20 79
 
4.2%
22 4
 
0.2%
21 2
 
0.1%
ValueCountFrequency (%)
0 496
26.4%
14 88
 
4.7%
15 201
10.7%
16 285
15.2%
17 366
19.5%
18 242
12.9%
19 114
 
6.1%
20 79
 
4.2%
21 2
 
0.1%
22 4
 
0.2%
ValueCountFrequency (%)
22 4
 
0.2%
21 2
 
0.1%
20 79
 
4.2%
19 114
 
6.1%
18 242
12.9%
17 366
19.5%
16 285
15.2%
15 201
10.7%
14 88
 
4.7%
0 496
26.4%

body_type
Categorical

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Suv
742 
Sedan
510 
Hatchback
449 
Coupe
 
51
Minivan
 
45
Other values (5)
80 

Length

Max length11
Median length9
Mean length5.2759723
Min length3

Characters and Unicode

Total characters9903
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSedan
2nd rowSedan
3rd rowSedan
4th rowSuv
5th rowSuv

Common Values

ValueCountFrequency (%)
Suv 742
39.5%
Sedan 510
27.2%
Hatchback 449
23.9%
Coupe 51
 
2.7%
Minivan 45
 
2.4%
Pickup 40
 
2.1%
Convertible 15
 
0.8%
Wagon 13
 
0.7%
Van 7
 
0.4%
Targa 5
 
0.3%

Length

2024-06-20T01:32:43.870743image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:44.393252image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
suv 742
39.5%
sedan 510
27.2%
hatchback 449
23.9%
coupe 51
 
2.7%
minivan 45
 
2.4%
pickup 40
 
2.1%
convertible 15
 
0.8%
wagon 13
 
0.7%
van 7
 
0.4%
targa 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 1483
15.0%
S 1252
12.6%
c 938
9.5%
u 833
8.4%
v 802
8.1%
n 635
 
6.4%
e 591
 
6.0%
d 510
 
5.1%
k 489
 
4.9%
t 464
 
4.7%
Other values (15) 1906
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1483
15.0%
S 1252
12.6%
c 938
9.5%
u 833
8.4%
v 802
8.1%
n 635
 
6.4%
e 591
 
6.0%
d 510
 
5.1%
k 489
 
4.9%
t 464
 
4.7%
Other values (15) 1906
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1483
15.0%
S 1252
12.6%
c 938
9.5%
u 833
8.4%
v 802
8.1%
n 635
 
6.4%
e 591
 
6.0%
d 510
 
5.1%
k 489
 
4.9%
t 464
 
4.7%
Other values (15) 1906
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1483
15.0%
S 1252
12.6%
c 938
9.5%
u 833
8.4%
v 802
8.1%
n 635
 
6.4%
e 591
 
6.0%
d 510
 
5.1%
k 489
 
4.9%
t 464
 
4.7%
Other values (15) 1906
19.2%

rear_window
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1855 
No
 
22

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1855
98.8%
No 22
 
1.2%

Length

2024-06-20T01:32:44.889036image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:45.376950image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1855
98.8%
no 22
 
1.2%

Most occurring characters

ValueCountFrequency (%)
S 1855
49.4%
í 1855
49.4%
N 22
 
0.6%
o 22
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1855
49.4%
í 1855
49.4%
N 22
 
0.6%
o 22
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1855
49.4%
í 1855
49.4%
N 22
 
0.6%
o 22
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1855
49.4%
í 1855
49.4%
N 22
 
0.6%
o 22
 
0.6%

lane_departure_warning
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1704 
Sí
173 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowNo
3rd rowSí
4th rowNo
5th rowSí

Common Values

ValueCountFrequency (%)
No 1704
90.8%
Sí 173
 
9.2%

Length

2024-06-20T01:32:45.721413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:46.191656image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1704
90.8%
sí 173
 
9.2%

Most occurring characters

ValueCountFrequency (%)
N 1704
45.4%
o 1704
45.4%
S 173
 
4.6%
í 173
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1704
45.4%
o 1704
45.4%
S 173
 
4.6%
í 173
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1704
45.4%
o 1704
45.4%
S 173
 
4.6%
í 173
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1704
45.4%
o 1704
45.4%
S 173
 
4.6%
í 173
 
4.6%

isofix_anchor
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1690 
No
187 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1690
90.0%
No 187
 
10.0%

Length

2024-06-20T01:32:46.579682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:46.893478image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1690
90.0%
no 187
 
10.0%

Most occurring characters

ValueCountFrequency (%)
S 1690
45.0%
í 1690
45.0%
N 187
 
5.0%
o 187
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1690
45.0%
í 1690
45.0%
N 187
 
5.0%
o 187
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1690
45.0%
í 1690
45.0%
N 187
 
5.0%
o 187
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1690
45.0%
í 1690
45.0%
N 187
 
5.0%
o 187
 
5.0%

brake_assist
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1874 
Sí
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1874
99.8%
Sí 3
 
0.2%

Length

2024-06-20T01:32:47.555511image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:47.993251image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1874
99.8%
sí 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 1874
49.9%
o 1874
49.9%
S 3
 
0.1%
í 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1874
49.9%
o 1874
49.9%
S 3
 
0.1%
í 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1874
49.9%
o 1874
49.9%
S 3
 
0.1%
í 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1874
49.9%
o 1874
49.9%
S 3
 
0.1%
í 3
 
0.1%

front_airbags
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1716 
Sí
 
161

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSí
3rd rowSí
4th rowSí
5th rowNo

Common Values

ValueCountFrequency (%)
No 1716
91.4%
Sí 161
 
8.6%

Length

2024-06-20T01:32:48.387233image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:48.883961image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1716
91.4%
sí 161
 
8.6%

Most occurring characters

ValueCountFrequency (%)
N 1716
45.7%
o 1716
45.7%
S 161
 
4.3%
í 161
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1716
45.7%
o 1716
45.7%
S 161
 
4.3%
í 161
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1716
45.7%
o 1716
45.7%
S 161
 
4.3%
í 161
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1716
45.7%
o 1716
45.7%
S 161
 
4.3%
í 161
 
4.3%

side_airbags
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1739 
Sí
 
138

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowNo

Common Values

ValueCountFrequency (%)
No 1739
92.6%
Sí 138
 
7.4%

Length

2024-06-20T01:32:49.285337image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:49.767801image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1739
92.6%
sí 138
 
7.4%

Most occurring characters

ValueCountFrequency (%)
N 1739
46.3%
o 1739
46.3%
S 138
 
3.7%
í 138
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1739
46.3%
o 1739
46.3%
S 138
 
3.7%
í 138
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1739
46.3%
o 1739
46.3%
S 138
 
3.7%
í 138
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1739
46.3%
o 1739
46.3%
S 138
 
3.7%
í 138
 
3.7%

hill_descent_control
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1866 
Sí
 
11

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1866
99.4%
Sí 11
 
0.6%

Length

2024-06-20T01:32:50.191486image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:50.675568image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1866
99.4%
sí 11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
N 1866
49.7%
o 1866
49.7%
S 11
 
0.3%
í 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1866
49.7%
o 1866
49.7%
S 11
 
0.3%
í 11
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1866
49.7%
o 1866
49.7%
S 11
 
0.3%
í 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1866
49.7%
o 1866
49.7%
S 11
 
0.3%
í 11
 
0.3%

cruise_control
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1875 
Sí
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1875
99.9%
Sí 2
 
0.1%

Length

2024-06-20T01:32:51.072099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:51.563891image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1875
99.9%
sí 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1875
49.9%
o 1875
49.9%
S 2
 
0.1%
í 2
 
0.1%

traction_control
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Sí
1460 
No
417 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
Sí 1460
77.8%
No 417
 
22.2%

Length

2024-06-20T01:32:51.969739image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:52.465313image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sí 1460
77.8%
no 417
 
22.2%

Most occurring characters

ValueCountFrequency (%)
S 1460
38.9%
í 1460
38.9%
N 417
 
11.1%
o 417
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1460
38.9%
í 1460
38.9%
N 417
 
11.1%
o 417
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1460
38.9%
í 1460
38.9%
N 417
 
11.1%
o 417
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1460
38.9%
í 1460
38.9%
N 417
 
11.1%
o 417
 
11.1%

disc_brakes
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1873 
Sí
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1873
99.8%
Sí 4
 
0.2%

Length

2024-06-20T01:32:52.870424image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:53.358969image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1873
99.8%
sí 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 1873
49.9%
o 1873
49.9%
S 4
 
0.1%
í 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1873
49.9%
o 1873
49.9%
S 4
 
0.1%
í 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1873
49.9%
o 1873
49.9%
S 4
 
0.1%
í 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1873
49.9%
o 1873
49.9%
S 4
 
0.1%
í 4
 
0.1%

automatic_collision_braking
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1678 
Sí
199 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowNo
3rd rowSí
4th rowNo
5th rowSí

Common Values

ValueCountFrequency (%)
No 1678
89.4%
Sí 199
 
10.6%

Length

2024-06-20T01:32:53.772227image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:54.265670image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1678
89.4%
sí 199
 
10.6%

Most occurring characters

ValueCountFrequency (%)
N 1678
44.7%
o 1678
44.7%
S 199
 
5.3%
í 199
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1678
44.7%
o 1678
44.7%
S 199
 
5.3%
í 199
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1678
44.7%
o 1678
44.7%
S 199
 
5.3%
í 199
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1678
44.7%
o 1678
44.7%
S 199
 
5.3%
í 199
 
5.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1307 
Sí
570 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowSí
5th rowSí

Common Values

ValueCountFrequency (%)
No 1307
69.6%
Sí 570
30.4%

Length

2024-06-20T01:32:54.699745image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:55.176125image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1307
69.6%
sí 570
30.4%

Most occurring characters

ValueCountFrequency (%)
N 1307
34.8%
o 1307
34.8%
S 570
15.2%
í 570
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1307
34.8%
o 1307
34.8%
S 570
15.2%
í 570
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1307
34.8%
o 1307
34.8%
S 570
15.2%
í 570
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1307
34.8%
o 1307
34.8%
S 570
15.2%
í 570
15.2%

abs_brakes
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1832 
Sí
 
45

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1832
97.6%
Sí 45
 
2.4%

Length

2024-06-20T01:32:55.584764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:56.070658image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1832
97.6%
sí 45
 
2.4%

Most occurring characters

ValueCountFrequency (%)
N 1832
48.8%
o 1832
48.8%
S 45
 
1.2%
í 45
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1832
48.8%
o 1832
48.8%
S 45
 
1.2%
í 45
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1832
48.8%
o 1832
48.8%
S 45
 
1.2%
í 45
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1832
48.8%
o 1832
48.8%
S 45
 
1.2%
í 45
 
1.2%

disc_brakes.1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1876 
Sí
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1876
99.9%
Sí 1
 
0.1%

Length

2024-06-20T01:32:56.475739image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:56.906099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1876
99.9%
sí 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1876
50.0%
o 1876
50.0%
S 1
 
< 0.1%
í 1
 
< 0.1%

stop_go_function
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1832 
Sí
 
45

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1832
97.6%
Sí 45
 
2.4%

Length

2024-06-20T01:32:57.300549image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:57.767292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1832
97.6%
sí 45
 
2.4%

Most occurring characters

ValueCountFrequency (%)
N 1832
48.8%
o 1832
48.8%
S 45
 
1.2%
í 45
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1832
48.8%
o 1832
48.8%
S 45
 
1.2%
í 45
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1832
48.8%
o 1832
48.8%
S 45
 
1.2%
í 45
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1832
48.8%
o 1832
48.8%
S 45
 
1.2%
í 45
 
1.2%

electric_locks
Categorical

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Remoto
932 
Llave de la tarjeta
813 
NoInfo
 
66
Llave
 
30
Teléfono móvil
 
26
Other values (2)
 
10

Length

Max length19
Median length6
Mean length11.760788
Min length5

Characters and Unicode

Total characters22075
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLlave de la tarjeta
2nd rowLlave de la tarjeta
3rd rowTeléfono móvil
4th rowLlave de la tarjeta
5th rowLlave de la tarjeta

Common Values

ValueCountFrequency (%)
Remoto 932
49.7%
Llave de la tarjeta 813
43.3%
NoInfo 66
 
3.5%
Llave 30
 
1.6%
Teléfono móvil 26
 
1.4%
Teclado digital 7
 
0.4%
Interno 3
 
0.2%

Length

2024-06-20T01:32:58.104228image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:58.669764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
remoto 932
21.4%
llave 843
19.4%
de 813
18.7%
la 813
18.7%
tarjeta 813
18.7%
noinfo 66
 
1.5%
teléfono 26
 
0.6%
móvil 26
 
0.6%
teclado 7
 
0.2%
digital 7
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 3437
15.6%
a 3296
14.9%
t 2568
11.6%
2472
11.2%
o 2058
9.3%
l 1722
7.8%
m 958
 
4.3%
R 932
 
4.2%
v 869
 
3.9%
L 843
 
3.8%
Other values (13) 2920
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3437
15.6%
a 3296
14.9%
t 2568
11.6%
2472
11.2%
o 2058
9.3%
l 1722
7.8%
m 958
 
4.3%
R 932
 
4.2%
v 869
 
3.9%
L 843
 
3.8%
Other values (13) 2920
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3437
15.6%
a 3296
14.9%
t 2568
11.6%
2472
11.2%
o 2058
9.3%
l 1722
7.8%
m 958
 
4.3%
R 932
 
4.2%
v 869
 
3.9%
L 843
 
3.8%
Other values (13) 2920
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3437
15.6%
a 3296
14.9%
t 2568
11.6%
2472
11.2%
o 2058
9.3%
l 1722
7.8%
m 958
 
4.3%
R 932
 
4.2%
v 869
 
3.9%
L 843
 
3.8%
Other values (13) 2920
13.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
No
1621 
Sí
256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSí
2nd rowSí
3rd rowSí
4th rowNo
5th rowSí

Common Values

ValueCountFrequency (%)
No 1621
86.4%
Sí 256
 
13.6%

Length

2024-06-20T01:32:59.158985image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T01:32:59.593970image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
no 1621
86.4%
sí 256
 
13.6%

Most occurring characters

ValueCountFrequency (%)
N 1621
43.2%
o 1621
43.2%
S 256
 
6.8%
í 256
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1621
43.2%
o 1621
43.2%
S 256
 
6.8%
í 256
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1621
43.2%
o 1621
43.2%
S 256
 
6.8%
í 256
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1621
43.2%
o 1621
43.2%
S 256
 
6.8%
í 256
 
6.8%
Distinct1035
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
2024-06-20T01:33:00.197908image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Length

Max length40
Median length32
Mean length16.952584
Min length3

Characters and Unicode

Total characters31820
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique620 ?
Unique (%)33.0%

Sample

1st row3.5 S 400 L CGI AT
2nd row3.0 PREMIUM AUTO 4WD
3rd row2.0 SPORT 300 AUTO 4WD
4th row1.4 XCELLENCE DCT
5th row2.0 MOMENTUM T6 AWD AUTO
ValueCountFrequency (%)
auto 503
 
7.0%
2.0 399
 
5.6%
at 389
 
5.4%
1.6 331
 
4.6%
1.5 176
 
2.5%
1.4 170
 
2.4%
2.5 153
 
2.1%
sport 145
 
2.0%
mt 140
 
2.0%
4wd 130
 
1.8%
Other values (477) 4614
64.5%
2024-06-20T01:33:01.472490image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5273
16.6%
T 2697
 
8.5%
. 1888
 
5.9%
A 1821
 
5.7%
E 1615
 
5.1%
I 1570
 
4.9%
O 1445
 
4.5%
S 1097
 
3.4%
R 1063
 
3.3%
C 1057
 
3.3%
Other values (37) 12294
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5273
16.6%
T 2697
 
8.5%
. 1888
 
5.9%
A 1821
 
5.7%
E 1615
 
5.1%
I 1570
 
4.9%
O 1445
 
4.5%
S 1097
 
3.4%
R 1063
 
3.3%
C 1057
 
3.3%
Other values (37) 12294
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5273
16.6%
T 2697
 
8.5%
. 1888
 
5.9%
A 1821
 
5.7%
E 1615
 
5.1%
I 1570
 
4.9%
O 1445
 
4.5%
S 1097
 
3.4%
R 1063
 
3.3%
C 1057
 
3.3%
Other values (37) 12294
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5273
16.6%
T 2697
 
8.5%
. 1888
 
5.9%
A 1821
 
5.7%
E 1615
 
5.1%
I 1570
 
4.9%
O 1445
 
4.5%
S 1097
 
3.4%
R 1063
 
3.3%
C 1057
 
3.3%
Other values (37) 12294
38.6%

monthly_price
Real number (ℝ)

Distinct463
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4154.2824
Minimum2061
Maximum11871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:33:01.975108image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2061
5-th percentile2491
Q13101
median3824
Q34796
95-th percentile6948.4
Maximum11871
Range9810
Interquartile range (IQR)1695

Descriptive statistics

Standard deviation1439.8384
Coefficient of variation (CV)0.34659137
Kurtosis2.9202822
Mean4154.2824
Median Absolute Deviation (MAD)802
Skewness1.4681451
Sum7797588
Variance2073134.6
MonotonicityNot monotonic
2024-06-20T01:33:02.476806image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3395 17
 
0.9%
2977 15
 
0.8%
4118 14
 
0.7%
2411 14
 
0.7%
3282 14
 
0.7%
3429 13
 
0.7%
2818 12
 
0.6%
3022 12
 
0.6%
4129 12
 
0.6%
3180 12
 
0.6%
Other values (453) 1742
92.8%
ValueCountFrequency (%)
2061 1
0.1%
2084 1
0.1%
2118 1
0.1%
2129 1
0.1%
2174 1
0.1%
2208 2
0.1%
2219 2
0.1%
2231 1
0.1%
2242 2
0.1%
2253 1
0.1%
ValueCountFrequency (%)
11871 1
0.1%
11453 1
0.1%
10933 1
0.1%
10877 1
0.1%
10481 1
0.1%
10402 1
0.1%
10233 1
0.1%
10210 1
0.1%
9939 1
0.1%
9882 1
0.1%

price
Real number (ℝ)

Distinct464
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311191.86
Minimum125999
Maximum993999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:33:02.970990image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum125999
5-th percentile163999
Q1217999
median281999
Q3367999
95-th percentile558399
Maximum993999
Range868000
Interquartile range (IQR)150000

Descriptive statistics

Standard deviation127381.74
Coefficient of variation (CV)0.40933505
Kurtosis2.9218148
Mean311191.86
Median Absolute Deviation (MAD)71000
Skewness1.4683234
Sum5.8410712 × 108
Variance1.6226107 × 1010
MonotonicityNot monotonic
2024-06-20T01:33:03.624957image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243999 17
 
0.9%
206999 15
 
0.8%
233999 14
 
0.7%
156999 14
 
0.7%
307999 14
 
0.7%
246999 13
 
0.7%
224999 12
 
0.6%
210999 12
 
0.6%
279999 12
 
0.6%
308999 12
 
0.6%
Other values (454) 1742
92.8%
ValueCountFrequency (%)
125999 1
0.1%
127999 1
0.1%
130999 1
0.1%
131999 1
0.1%
135999 1
0.1%
138999 2
0.1%
139999 2
0.1%
140999 1
0.1%
141999 2
0.1%
142999 1
0.1%
ValueCountFrequency (%)
993999 1
0.1%
956999 1
0.1%
910999 1
0.1%
905999 1
0.1%
870999 1
0.1%
863999 1
0.1%
848999 1
0.1%
846999 1
0.1%
822999 1
0.1%
817999 1
0.1%

km
Real number (ℝ)

Distinct1611
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64277.518
Minimum4300
Maximum173850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:33:04.083099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum4300
5-th percentile22103.2
Q141876
median62600
Q385082
95-th percentile112012.6
Maximum173850
Range169550
Interquartile range (IQR)43206

Descriptive statistics

Standard deviation28177.393
Coefficient of variation (CV)0.4383709
Kurtosis-0.61934688
Mean64277.518
Median Absolute Deviation (MAD)21400
Skewness0.26028583
Sum1.206489 × 108
Variance7.939655 × 108
MonotonicityNot monotonic
2024-06-20T01:33:04.587544image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41000 6
 
0.3%
71000 6
 
0.3%
92000 6
 
0.3%
53000 6
 
0.3%
95000 6
 
0.3%
70000 5
 
0.3%
58681 5
 
0.3%
84000 5
 
0.3%
104000 5
 
0.3%
39000 5
 
0.3%
Other values (1601) 1822
97.1%
ValueCountFrequency (%)
4300 1
0.1%
7400 1
0.1%
7628 1
0.1%
8496 1
0.1%
9000 1
0.1%
9403 1
0.1%
9892 1
0.1%
10400 1
0.1%
10593 1
0.1%
11226 1
0.1%
ValueCountFrequency (%)
173850 1
0.1%
150300 1
0.1%
146000 1
0.1%
138200 1
0.1%
137500 1
0.1%
131400 1
0.1%
131130 1
0.1%
131000 1
0.1%
130943 1
0.1%
130671 1
0.1%

brand
Categorical

Distinct38
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Chevrolet
156 
Volkswagen
145 
Nissan
143 
Mazda
 
120
Honda
 
113
Other values (33)
1200 

Length

Max length13
Median length9
Mean length6.0596697
Min length2

Characters and Unicode

Total characters11374
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMercedes Benz
2nd rowAudi
3rd rowJaguar Xe
4th rowSeat
5th rowVolvo

Common Values

ValueCountFrequency (%)
Chevrolet 156
 
8.3%
Volkswagen 145
 
7.7%
Nissan 143
 
7.6%
Mazda 120
 
6.4%
Honda 113
 
6.0%
Kia 104
 
5.5%
Ford 104
 
5.5%
Bmw 95
 
5.1%
Audi 94
 
5.0%
Toyota 87
 
4.6%
Other values (28) 716
38.1%

Length

2024-06-20T01:33:05.065762image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chevrolet 156
 
7.9%
volkswagen 145
 
7.3%
nissan 143
 
7.2%
mazda 120
 
6.1%
honda 113
 
5.7%
kia 104
 
5.3%
ford 104
 
5.3%
bmw 95
 
4.8%
audi 94
 
4.7%
toyota 87
 
4.4%
Other values (33) 819
41.4%

Most occurring characters

ValueCountFrequency (%)
e 1125
 
9.9%
a 1123
 
9.9%
o 834
 
7.3%
i 745
 
6.6%
n 726
 
6.4%
d 640
 
5.6%
s 560
 
4.9%
t 447
 
3.9%
l 410
 
3.6%
r 400
 
3.5%
Other values (35) 4364
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1125
 
9.9%
a 1123
 
9.9%
o 834
 
7.3%
i 745
 
6.6%
n 726
 
6.4%
d 640
 
5.6%
s 560
 
4.9%
t 447
 
3.9%
l 410
 
3.6%
r 400
 
3.5%
Other values (35) 4364
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1125
 
9.9%
a 1123
 
9.9%
o 834
 
7.3%
i 745
 
6.6%
n 726
 
6.4%
d 640
 
5.6%
s 560
 
4.9%
t 447
 
3.9%
l 410
 
3.6%
r 400
 
3.5%
Other values (35) 4364
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1125
 
9.9%
a 1123
 
9.9%
o 834
 
7.3%
i 745
 
6.6%
n 726
 
6.4%
d 640
 
5.6%
s 560
 
4.9%
t 447
 
3.9%
l 410
 
3.6%
r 400
 
3.5%
Other values (35) 4364
38.4%

age
Real number (ℝ)

Distinct13
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4198189
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2024-06-20T01:33:05.365693image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median7
Q39
95-th percentile11
Maximum15
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2371622
Coefficient of variation (CV)0.3015117
Kurtosis-0.2579253
Mean7.4198189
Median Absolute Deviation (MAD)2
Skewness0.32092318
Sum13927
Variance5.0048948
MonotonicityNot monotonic
2024-06-20T01:33:05.770494image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7 326
17.4%
6 294
15.7%
8 281
15.0%
9 241
12.8%
5 233
12.4%
10 161
8.6%
4 117
 
6.2%
11 103
 
5.5%
12 47
 
2.5%
3 45
 
2.4%
Other values (3) 29
 
1.5%
ValueCountFrequency (%)
3 45
 
2.4%
4 117
 
6.2%
5 233
12.4%
6 294
15.7%
7 326
17.4%
8 281
15.0%
9 241
12.8%
10 161
8.6%
11 103
 
5.5%
12 47
 
2.5%
ValueCountFrequency (%)
15 2
 
0.1%
14 8
 
0.4%
13 19
 
1.0%
12 47
 
2.5%
11 103
 
5.5%
10 161
8.6%
9 241
12.8%
8 281
15.0%
7 326
17.4%
6 294
15.7%

Interactions

2024-06-20T01:31:53.885104image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:05.698821image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:10.289651image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:14.563270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:19.058717image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:23.290552image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:27.678551image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:32.076645image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:36.381967image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:40.672994image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:44.985360image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:49.594559image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:54.283259image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:06.075917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:10.686224image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:14.902637image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:19.393571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:23.678339image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:28.072678image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:32.396263image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:36.780530image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:40.990449image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:45.358658image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:49.960702image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:54.678224image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:06.492244image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:11.080493image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:15.276680image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:19.768929image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:24.076087image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:28.398668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:32.779110image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:37.169428image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:41.363265image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:45.686134image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:50.278001image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:55.060963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:06.868586image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:11.472388image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:15.592927image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:20.102674image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:24.461630image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:28.782202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:33.098595image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:37.501152image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:41.679479image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:46.080550image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:50.597538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:55.373655image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:07.195237image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:11.777200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:15.980086image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:20.473877image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:24.783046image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:29.087465image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:33.464110image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:37.877387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:42.079287image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:46.384912image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:50.979625image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:55.674394image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:07.580342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:12.079864image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:16.377617image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:20.863064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:25.175144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:29.459103image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:33.786330image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:38.272866image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:42.461362image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:46.926482image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:51.374146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:56.078968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:07.896372image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:12.393922image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:16.782174image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:21.189250image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:25.508956image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:29.769853image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:34.187659image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:38.597781image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:42.778091image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:47.282719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:51.685542image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:56.490127image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:08.284501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:12.780129image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:17.291977image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:21.496256image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:25.882221image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:30.105300image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:34.580130image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:38.886222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:43.159869image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:47.681861image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:52.064732image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:56.879413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:08.680361image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:13.169222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:17.677535image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:21.878130image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:26.269416image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:30.476289image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:34.978400image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:39.190146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:43.471771image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:48.080096image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:52.380442image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:57.283432image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:09.093101image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:13.482405image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:18.059516image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:22.206387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:26.591116image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:30.875140image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:35.298917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:39.581350image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:43.862252image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:48.481014image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:52.785652image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:57.677293image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:09.478873image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:13.874135image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:18.373126image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:22.575722image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:26.978425image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:31.189062image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:35.588690image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:39.977647image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:44.189218image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:48.872937image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:53.185341image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:58.065567image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:09.884078image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:14.187241image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:18.680817image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:22.893086image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:27.289790image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:31.699510image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:35.984218image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:40.300225image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:44.583558image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:49.277477image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-20T01:31:53.568665image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Missing values

2024-06-20T01:31:58.875929image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-20T01:31:59.782857image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fuel_consumption_km_ltracciontransmisionauxandroid_autoapple_carplaybluetoothcddvdhdmitouch_screenradionavigation_systemsd_cardacalarmkeyless_entrypush_startcamerapower_mirrorssteering_wheel_controlsfront_cupholdersrear_cupholderssunrooffront_sensorrear_sensorrain_sensorpanoramic_roofexternal_temperaturehorsepowerfuel_typedisplacementgearsstart_stopengine_typetorqueturbomax_speedpower_windows_frontpower_windows_rearextrasdriver_seat_adjustmentrearview_mirror_cameraseat_materialpassengerstrunk_openingheadlightswheel_materialdoorswheelsbody_typerear_windowlane_departure_warningisofix_anchorbrake_assistfront_airbagsside_airbagshill_descent_controlcruise_controltraction_controldisc_brakesautomatic_collision_brakingelectric_parking_brakeabs_brakesdisc_brakes.1stop_go_functionelectric_locksanti_collision_systemmodel_describemonthly_pricepricekmbrandage
074x2AutomáticoSíNoNoSíNoNoNoSíSíSíSíSíNoNoSíNoNoNoSíSíSíSíSíNoNoSí449estándar3.07SíCombustión516Sí250SíSíNoNoNoPiel5RemotaEstándarAluminio40SedanSíSíSíNoNoSíNoNoSíNoSíSíNoNoSíLlave de la tarjetaSí3.5 S 400 L CGI AT6865550999110980Mercedes Benz9
1104x4AutomáticoSíNoNoSíSíNoNoNoSíSíSíSíNoNoSíNoNoNoSíSíSíSíSíNoNoSí310estándar3.08SíCombustión325Sí250SíSíNoNoNoPiel premium5RemotaNoInfoAluminio419SedanSíNoSíNoSíSíNoNoSíNoNoSíNoNoNoLlave de la tarjetaSí3.0 PREMIUM AUTO 4WD810866099946520Audi7
2114x4AutomáticoNoNoNoSíNoNoNoSíSíSíNoSíNoNoSíNoNoNoSíSíSíSíSíNoNoSí300estándar2.08SíCombustión295Sí250SíSíNoNoNoPiel5RemotaNoInfoAluminio40SedanSíSíSíNoSíSíNoNoSíNoSíSíNoNoNoTeléfono móvilSí2.0 SPORT 300 AUTO 4WD979280999918850Jaguar Xe6
3174x2AutomáticoSíSíSíSíNoNoNoSíSíNoSíSíNoNoSíNoNoNoSíSíNoSíSíNoNoSí150estándar1.47SíCombustión148Sí198SíSíNoNoNoGamuza Sintética5RemotaEstándarAluminio518SuvSíNoSíNoSíSíNoNoSíNoNoSíNoNoNoLlave de la tarjetaNo1.4 XCELLENCE DCT477436599955259Seat6
4124x4AutomáticoSíNoNoNoNoNoNoSíSíSíNoSíNoNoSíNoNoNoSíSíSíSíSíNoNoSí320Gasolina2.08SíCombustión295Sí230SíSíNoNoNoPiel7RemotaEstándarAluminio520SuvSíSíSíNoNoNoNoNoSíNoSíSíNoNoNoLlave de la tarjetaSí2.0 MOMENTUM T6 AWD AUTO7192579999109560Volvo7
594x4AutomáticoSíNoNoNoNoNoNoNoSíSíSíSíNoNoSíNoNoNoSíSíSíSíSíNoNoSí302Gasolina3.57SíCombustión273No235SíSíNoNoNoPiel5RemotaEstándarAluminio50SuvSíSíSíNoNoSíNoNoSíNoSíSíNoNoNoLlave de la tarjetaSí3.5 ML 350 CGI SPORT AMG AT463835399966000Mercedes Benz11
684x4AutomáticoNoNoNoNoNoNoNoNoSíSíSíSíNoNoSíNoNoNoSíSíSíSíSíNoNoSí328Gasolina3.07SíCombustión354Sí247SíSíNoNoNoCuero Sintético5RemotaNoInfoAluminio50SuvSíSíSíNoNoSíNoNoSíNoSíSíNoNoNoLlave de la tarjetaSí3.0 GLE 400 CGI SPORT 4WD AT748660599966100Mercedes Benz8
7134x2AutomáticoNoSíSíNoNoNoNoSíSíSíNoSíNoNoSíNoNoNoSíSíNoSíSíNoNoSí190Gasolina2.08SíCombustión221Sí210SíSíNoNoNoPiel5RemotaEstándarAluminio518SuvSíSíSíNoNoNoNoNoSíNoSíSíNoNoNoLlave de la tarjetaSí2.0 T4 MOMENTUM AUTO591546699980717Volvo5
894x2AutomáticoNoSíSíNoNoNoNoSíSíSíNoSíNoNoSíNoNoNoSíSíSíSíSíNoNoSí247Gasolina2.010NoCombustión273Sí0SíSíNoNoNoPiel5RemotaEstándarAluminio419SedanSíSíSíNoNoNoNoNoSíNoSíSíNoNoNoLlave de la tarjetaSí2.0 TOURING AUTO498838499944737Honda7
9144x4AutomáticoSíNoNoNoNoSíNoSíSíSíNoSíNoNoSíNoNoNoSíSíSíSíSíNoSíSí250estándar2.50SíEléctrico243Sí0SíSíNoNoNoPiel7RemotaEstándarAluminio520SuvSíSíSíNoNoNoNoNoSíNoSíNoNoNoNoLlave de la tarjetaSí2.5 HEV 4WD AUTO HYBRID563344199999100Infiniti8
fuel_consumption_km_ltracciontransmisionauxandroid_autoapple_carplaybluetoothcddvdhdmitouch_screenradionavigation_systemsd_cardacalarmkeyless_entrypush_startcamerapower_mirrorssteering_wheel_controlsfront_cupholdersrear_cupholderssunrooffront_sensorrear_sensorrain_sensorpanoramic_roofexternal_temperaturehorsepowerfuel_typedisplacementgearsstart_stopengine_typetorqueturbomax_speedpower_windows_frontpower_windows_rearextrasdriver_seat_adjustmentrearview_mirror_cameraseat_materialpassengerstrunk_openingheadlightswheel_materialdoorswheelsbody_typerear_windowlane_departure_warningisofix_anchorbrake_assistfront_airbagsside_airbagshill_descent_controlcruise_controltraction_controldisc_brakesautomatic_collision_brakingelectric_parking_brakeabs_brakesdisc_brakes.1stop_go_functionelectric_locksanti_collision_systemmodel_describemonthly_pricepricekmbrandage
186764x2AutomáticoSíNoNoNoNoNoNoNoNoNoNoSíNoNoNoNoNoNoSíSíNoSíSíNoNoSí381estándar5.76NoCombustión401No0NoNoNoNoSíNoInfo7EléctricaNoInfoNoInfo50SuvSíNoSíNoNoNoNoNoSíNoNoNoNoNoNoRemotoNo5.7 V8 PLATINUM422031699975505Toyota14
1868174x2ManualSíNoNoNoNoNoNoNoSíNoSíSíNoNoNoNoNoNoSíNoNoNoNoNoNoNo100Gasolina1.65NoCombustión105No160NoNoNoNoNoTela5NoInfoNoInfoNoInfo514HatchbackSíNoNoNoSíNoNoNoNoNoNoNoNoNoNoNoInfoNo1.6 5 PTAS. TRENDLINE2411156999112500Volkswagen8
1869164x2ManualSíNoNoNoNoNoNoNoSíNoNoSíNoNoNoNoNoNoSíSíNoNoNoNoNoNo140Gasolina1.85NoCombustión129No0SíSíNoNoNoTela5NoInfoNoInfoAleación516SuvSíNoNoNoNoNoNoNoNoNoNoNoNoNoNoRemotoNo1.8 A LS MT281819299984150Chevrolet10
1870174x2ManualSíNoNoNoNoNoNoNoSíNoNoSíNoNoNoNoNoNoSíNoNoNoNoNoNoNo107Gasolina1.65NoCombustión104No0SíSíNoNoNoTela5MecánicaNoInfoNoInfo40SedanSíNoSíNoNoNoNoNoNoNoNoNoNoNoNoLlaveNo1.5 LS A318022499923463Chevrolet3
1871174x2ManualSíNoNoNoNoNoNoNoSíNoSíSíNoNoNoNoNoNoSíNoNoNoNoNoNoNo100Gasolina1.65NoCombustión105No0SíNoNoNoNoTela5NoInfoNoInfoAleación514HatchbackSíNoNoNoNoNoNoNoNoNoNoNoNoNoNoLlaveNo1.6 5 PTAS. TRENDLINE254716899984000Volkswagen6
1872144x2ManualNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSíNoNoNoNoNoNoNo100Gasolina1.65NoCombustión105No160NoNoNoNoNoTela5MecánicaNoInfoAleación414SedanSíNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoInfoNo1.6 SEDAN CL231014799991732Volkswagen9
1873144x2ManualNoNoNoNoNoNoNoNoSíNoNoSíNoNoNoNoNoNoSíSíNoNoNoNoNoSí105estándar1.65NoCombustión107No0NoNoNoNoNoTela5NoInfoNoInfoNoInfo415SedanSíNoSíNoNoNoNoNoNoNoNoNoNoNoNoNoInfoNo1.6 LIFE245716099980100Renault7
1874154x2ManualSíNoNoNoNoNoNoNoSíNoNoSíNoNoNoNoNoNoSíSíNoNoNoNoNoNo115NoInfoNaN0NoNoInfo114No0NoNoNoNoNoTela5RemotaNoInfoAluminio515HatchbackSíNoSíNoSíNoNoNoNoNoNoNoNoNoNoRemotoNo1.6 MT J LT2468161999106616Chevrolet8
187594x4AutomáticoNoNoNoNoNoNoNoNoSíNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNo285estándarNaN0NoNoInfo300No0NoNoNoNoNoCuero Sintético2RemotaEstándarAluminio519SuvNoNoNoNoSíSíNoNoNoNoNoNoNoNoNoNoInfoNo2.3 RESERVE AWD AT4118307999104244Lincoln9
1876164x2AutomáticoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNo84NoInfoNaN0NoNoInfo89No0NoNoNoNoNoNoInfo2NoInfoNoInfoNoInfo20HatchbackNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoInfoNoFORTWO COUPÉ PASSION AT258117199954400Smart10